{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本书配套视频课程：[解剖深度学习原理，从0实现深度学习库](https://ke.qq.com/course/2900371?tuin=ac5537fd) \n",
    "\n",
    "更多代码或学习资料将向购买视频课程或书的学生提供。\n",
    "\n",
    "\n",
    "+ 博客网站：[https://hwdong-net.github.io](https://hwdong-net.github.io)\n",
    "+ youtube频道: [hwdong](http://www.youtube.com/c/hwdong)\n",
    "+ bilibili网站：[hw-dong](https://space.bilibili.com/281453312)\n",
    "\n",
    "# 第6章 卷积神经网络CNN\n",
    "## 6.1 卷积\n",
    "### 6.1.1 什么是卷积？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(5)\n",
    "x = np.random.randint(low=1, high=30, size=10,dtype='l')\n",
    "print(x)\n",
    "\n",
    "w = np.array([1.2,0.3,0.5])\n",
    "n = x.size\n",
    "K = w.size\n",
    "z = np.zeros(n-K+1)\n",
    "for i in range(n-K+1):   \n",
    "    z[i] = np.sum(x[i:i+K]*w)\n",
    "print(w)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv1d(x,w,pad):\n",
    "    n = x.size\n",
    "    K = w.size\n",
    "    P = 2*pad\n",
    "    n_o = n+P-K+1\n",
    "    y = np.zeros(n_o)\n",
    "    if P>0:\n",
    "        x_pad = np.zeros(n+P)   \n",
    "        x_pad[pad:-pad] = x\n",
    "    else: \n",
    "        x_pad = x   \n",
    "    \n",
    "    for i in range(n_o): \n",
    "        y[i] = np.sum(x_pad[i:i+K]*w)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = conv1d(x,w,1)    #same卷积\n",
    "print(x.size,w.size,y1.size)\n",
    "print(\"same: \", y1) \n",
    "\n",
    "y2 = conv1d(x,w,2)     #full卷积\n",
    "print(x.size,w.size,y2.size)\n",
    "print(\"full: \", y2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "带有跨度的卷积运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv1d(x,w,pad=0,s=1): \n",
    "    n = x.size\n",
    "    K = w.size   \n",
    "    n_o = (n+2*pad-K)//s+1\n",
    "    y = np.zeros(n_o)             #卷积结果   \n",
    "    \n",
    "    if not pad==0:\n",
    "        #x_pad = np.zeros(n+2*pad)\n",
    "        #x_pad[pad:-pad] = x    \n",
    "        x_pad = np.pad(x,[(pad,pad)], mode='constant')\n",
    "    else:\n",
    "        x_pad = x\n",
    "    \n",
    "    for i in range(n_o):     # Loop over every pixel of the image\n",
    "        y[i] = np.sum(x_pad[i*s:i*s+K]*w)            \n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y0 = conv1d(x,w,0) \n",
    "y1 = conv1d(x,w,0,s=2)    \n",
    "y2 = conv1d(x,w,1,s=2)  \n",
    "print(y0)\n",
    "print(y1)\n",
    "print(y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.2 一维信号的卷积\n",
    "\n",
    "卷积用于对数据如一维信号或二维图像等进行处理，可以去除数据中的噪声或得到数据里蕴含的某种特征。\n",
    "\n",
    "下面生成了2组数x和y，x是$[0,2\\pi]$上均匀分布的一组数（100个），而y是对应地正弦曲线sin(x)附近的数（即y是对正弦曲线的噪声采样）：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(0,2*np.pi,100)\n",
    "y = np.sin(x) + np.random.random(100) * 0.2\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面代码根据正态分布，生成一组权值向量（卷积核）w："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma=1.6986436005760381\n",
    "x_for_w = np.arange(-6, 6)\n",
    "w = np.exp(-(x_for_w) ** 2 / (2 * sigma ** 2))\n",
    "w/= sum(w)\n",
    "print(x_for_w)\n",
    "print([\"%0.2f\" % x for x in w])\n",
    "plt.bar(x_for_w, w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个权值向量w的中间值大、两边值小，并且所有权值之和为1，用这个权值向量w对y进行卷积："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#w = np.array([0.1,0.2, 0.5, 0.2, 0.1])\n",
    "yhat = np.correlate(y, w,\"same\")\n",
    "plt.plot(x,yhat, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.3 二维卷积"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image matrix size:  (233, 328, 3)\n",
      "image matrix size:  (233, 328)\n",
      "\n",
      " First 5 columns and rows of the color image matrix: \n",
      " [[[143 106  90]\n",
      "  [141 104  88]\n",
      "  [148 108  96]\n",
      "  [142 102  90]\n",
      "  [135  95  83]]\n",
      "\n",
      " [[108  78  68]\n",
      "  [105  77  66]\n",
      "  [107  77  67]\n",
      "  [100  72  61]\n",
      "  [ 92  62  54]]\n",
      "\n",
      " [[159 138 135]\n",
      "  [159 140 136]\n",
      "  [167 149 145]\n",
      "  [165 150 145]\n",
      "  [167 149 147]]\n",
      "\n",
      " [[225 213 215]\n",
      "  [226 216 217]\n",
      "  [221 215 217]\n",
      "  [220 216 217]\n",
      "  [221 215 217]]\n",
      "\n",
      " [[208 203 210]\n",
      "  [207 204 211]\n",
      "  [207 207 215]\n",
      "  [204 209 215]\n",
      "  [205 208 215]]]\n",
      "\n",
      " First 5 columns and rows of the gray image matrix: \n",
      " [[0.44199569 0.43415255 0.4534698  0.42994039 0.40248941]\n",
      " [0.3280549  0.32218392 0.32413333 0.30257608 0.26587529]\n",
      " [0.55782824 0.56372196 0.59818275 0.59932157 0.59874824]\n",
      " [0.84585961 0.8556749  0.84870275 0.8506749  0.84870275]\n",
      " [0.80222431 0.80447922 0.81402667 0.81713765 0.81516549]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage import io, transform\n",
    "from skimage.color import rgb2gray\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "img = io.imread('image.jpg')\n",
    "gray_img = rgb2gray(img) #  io.imread('./imgs/image.jpg', as_grey=True)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "ax = axes.ravel()\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(img)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(gray_img,cmap='gray')\n",
    "#img = io.imread('./imgs/lenna.png', as_grey=True)  # load the image as grayscale\n",
    "#plt.imshow(img, cmap='gray')\n",
    "print('image matrix size: ', img.shape)      # print the size of image\n",
    "print('image matrix size: ', gray_img.shape)      # print the size of image\n",
    "print('\\n First 5 columns and rows of the color image matrix: \\n', img[150:155,110:115])\n",
    "print('\\n First 5 columns and rows of the gray image matrix: \\n', gray_img[150:155,110:115]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "颜色值也可以从`[0,1]`区间的实数值转化为`[0,255]`的整数值，下面代码将灰度图像的像素值转化到`[0,255]`区间整数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "灰度矩阵的前5行5列的数值: \n",
      " [[112 110 115 109 102]\n",
      " [ 83  82  82  77  67]\n",
      " [142 143 152 152 152]\n",
      " [215 218 216 216 216]\n",
      " [204 205 207 208 207]]\n"
     ]
    }
   ],
   "source": [
    "gray_img2 = gray_img*255\n",
    "gray_imgs= gray_img2.astype(np.uint8)\n",
    "print('灰度矩阵的前5行5列的数值: \\n', gray_imgs[150:155,110:115]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面是对二维矩阵（图像的）进行“valid卷积”操作的代码实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convolve2d(X, K): \n",
    "    h, w = K.shape\n",
    "    Y = np.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1))\n",
    "    for i in range(Y.shape[0]):\n",
    "        for j in range(Y.shape[1]):           \n",
    "            Y[i, j] = (X[i: i + h, j: j + w] * K).sum() \n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:  [[2 3 0 7 9 5]\n",
      " [6 0 4 7 2 3]\n",
      " [8 1 0 3 2 6]\n",
      " [7 6 1 5 2 8]\n",
      " [9 5 1 8 3 7]\n",
      " [2 4 1 8 6 5]]\n",
      "K:  [[-1  0  1]\n",
      " [-2  0  2]\n",
      " [-1  0  1]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-14.,  20.,   7.,  -7.],\n",
       "       [-24.,  10.,   3.,   5.],\n",
       "       [-28.,   3.,   6.,   8.],\n",
       "       [-23.,   9.,  10.,  -2.]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= np.array([[2,3,0,7,9,5], [6,0,4,7,2,3], [8,1,0,3,2,6],\n",
    "             [7,6,1,5,2,8], [9,5,1,8,3,7], [2,4,1,8,6,5]])\n",
    "K = np.array([[-1,0,1],[-2,0,2],[-1,0,1]])\n",
    "print(\"X: \",X)\n",
    "print(\"K: \",K)\n",
    "convolve2d(X,K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用这个卷积核对图像进行卷积操作:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原图像大小： (233, 328)\n",
      "结果图像大小： (231, 326)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = gray_img\n",
    "kernel = np.array([[-1,0,1],\n",
    "                   [-2,0,2],\n",
    "                   [-1,0,1]])\n",
    "image_sharpen = convolve2d(image,kernel)\n",
    "plt.imshow(image_sharpen, cmap=plt.cm.gray)\n",
    "print(\"原图像大小：\",image.shape)\n",
    "print(\"结果图像大小：\",image_sharpen.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，结果图像的垂直特征被放大了。表明这个一个具有“垂直边缘提取”作用的卷积核。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的代码对图像的上下、左右各填充了P_h,P_w个0值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "H,W = X.shape\n",
    "P_h,P_w = 1,2\n",
    "X_padded = np.zeros((H + 2*P_h, W +2*P_w))   \n",
    "X_padded[P_h:-P_h, P_w:-P_w] = X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 2. 3. 0. 7. 9. 5. 0. 0.]\n",
      " [0. 0. 6. 0. 4. 7. 2. 3. 0. 0.]\n",
      " [0. 0. 8. 1. 0. 3. 2. 6. 0. 0.]\n",
      " [0. 0. 7. 6. 1. 5. 2. 8. 0. 0.]\n",
      " [0. 0. 9. 5. 1. 8. 3. 7. 0. 0.]\n",
      " [0. 0. 2. 4. 1. 8. 6. 5. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(X_padded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面代码对数组a的第一行前面填充一行0，而第1列的前面、最后一列的后面各填充1列0和2列0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]]\n",
      "[[0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 1. 1. 0. 0.]\n",
      " [0. 1. 1. 1. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[ 1.,  1.,  1.],\n",
    "              [ 1.,  1.,  1.]])\n",
    "b = np.pad(a, [(1, 0), (1, 2)], mode='constant')\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "same卷积"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convolve2d_same(X, K): \n",
    "    H,W = X.shape   \n",
    "    K_h,K_w = K.shape\n",
    "   \n",
    "    P_h = (K_h)//2         #左右边缘填充的宽度\n",
    "    P_w = (K_w)//2         #上下边缘填充的高度\n",
    "    #Y = np.zeros_like(X)            #为什么这样会出错？\n",
    "    Y = np.zeros((H,W)) \n",
    "   \n",
    "    X_padded = np.pad(X, [(P_h, P_h), (P_w, P_w)], mode='constant')    \n",
    " #   X_padded = np.zeros((H + 2*P_h, W + 2*P_w))   \n",
    " #   X_padded[P_h:-P_h, P_w:-P_w] = X\n",
    "\n",
    "    for i in range(Y.shape[0]):  \n",
    "        for j in range(Y.shape[1]):\n",
    "            Y[i,j]=(X_padded[i:i+K_h,j:j+K_w]*K).sum()     \n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.,  -6.,  15.,  16.,  -8., -20.],\n",
       "       [  4., -14.,  20.,   7.,  -7., -15.],\n",
       "       [  8., -24.,  10.,   3.,   5.,  -8.],\n",
       "       [ 18., -28.,   3.,   6.,   8.,  -9.],\n",
       "       [ 20., -23.,   9.,  10.,  -2., -14.],\n",
       "       [ 13., -10.,  11.,  12.,  -7., -15.]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convolve2d_same(X,K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用这个“same卷积”操作对原图像执行卷积操作，将产生和原图像一样大小的结果图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原图像大小： (233, 328)\n",
      "结果图像大小： (233, 328)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = gray_img\n",
    "kernel = np.array([[-1,0,1],\n",
    "                   [-2,0,2],\n",
    "                   [-1,0,1]])\n",
    "image_sharpen = convolve2d_same(image,kernel)\n",
    "plt.imshow(image_sharpen, cmap=plt.cm.gray)\n",
    "print(\"原图像大小：\",image.shape)\n",
    "print(\"结果图像大小：\",image_sharpen.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同的卷积核对图像进行卷积将产生不一样的结果，比如可以用一个可以“提取边缘”的卷积核对图像进行卷积"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2543009ed00>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = np.array([[-1,-1,-1],\n",
    "                   [-1,8,-1],\n",
    "                   [-1,-1,-1]])\n",
    "edges = convolve2d_same(image,kernel)\n",
    "plt.imshow(edges, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scipy.signal模块有一个类似的函数convolve2d().和我们自己实现的卷积不同的是，它会先对卷积图像进行左右和上下翻转，然后再和卷积核对应元素值执行累加和。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2542eeca820>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.signal\n",
    "kernel = np.flipud(np.fliplr(kernel))    # 翻转卷积核\n",
    "edges =scipy.signal.convolve2d(image, kernel, 'same')\n",
    "plt.imshow(edges, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用一个具有平滑作用的卷积核对图像进行平滑处理，这个卷积核用一个像素周围25个像素的平均值作为该像素的值。平滑处理后图像变得模糊了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.11111111 0.11111111 0.11111111 0.11111111 0.11111111]\n",
      " [0.11111111 0.11111111 0.11111111 0.11111111 0.11111111]\n",
      " [0.11111111 0.11111111 0.11111111 0.11111111 0.11111111]\n",
      " [0.11111111 0.11111111 0.11111111 0.11111111 0.11111111]\n",
      " [0.11111111 0.11111111 0.11111111 0.11111111 0.11111111]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2542ee179d0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = 1./9*np.ones((5,5)) \n",
    "print(kernel)\n",
    "edges = convolve2d_same(image,kernel)\n",
    "plt.imshow(edges, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 跨度\n",
    "\n",
    "上述的卷积操作的**跨度**是1，\n",
    "\n",
    "$$\\begin{aligned}(H-F_h+P_h)/S_h+1 \\\\(W-F_w+P_w)/S_w+1\\end{aligned}\\tag{6-4}$$\n",
    "\n",
    "即综合填充和跨度的二维卷积操作的python代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convolve2d(X, K,pad=(0,0),stride = (1,1)): \n",
    "    H,W = X.shape   \n",
    "    K_h,K_w = K.shape\n",
    "   \n",
    "    P_h,P_w = pad\n",
    "    S_h,S_w = stride  \n",
    "   \n",
    "    h = (H-K_h+2*P_h)//S_h+1\n",
    "    w = (W-K_w+2*P_w)//S_w+1    \n",
    "    Y = np.zeros((h,w)) \n",
    "   \n",
    "    if P_h!=0 or  P_w !=0:\n",
    "        X_padded = np.pad(X, [(P_h, P_h), (P_w, P_w)], mode='constant') \n",
    "    else:\n",
    "        X_padded = X\n",
    "    for i in range(Y.shape[0]):\n",
    "        hs = i*S_h\n",
    "        for j in range(Y.shape[1]):\n",
    "            ws = j*S_w\n",
    "            Y[i,j]=(X_padded[hs:hs+K_h,ws:ws+K_w]*K).sum()     \n",
    "    return Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对之前的二维矩阵和卷积核K执行下面的卷积操作的结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6., 15., -8.],\n",
       "       [ 8., 10.,  5.],\n",
       "       [20.,  9., -2.]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= np.array([[2,3,0,7,9,5], [6,0,4,7,2,3], [8,1,0,3,2,6],[7,6,1,5,2,8], [9,5,1,8,3,7], [2,4,1,8,6,5]])\n",
    "convolve2d(X,K,(1,1),(2,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对图像执行下面的卷积核操作，该卷积核将像素自身的5倍数值减去其四周邻近像素的值，上下、左右的跨度为2，结果图像的高宽几乎是原图像高度的一半。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原图像大小： (233, 328)\n",
      "结果图像大小： (117, 164)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = gray_img\n",
    "kernel = np.array([[0,-1,0],\n",
    "                   [-1,5,-1],\n",
    "                   [0,-1,0]])\n",
    "image_filtered = convolve2d(image,kernel,(1,1),(2,2))\n",
    "plt.imshow(image_filtered, cmap=plt.cm.gray)\n",
    "print(\"原图像大小：\",image.shape)\n",
    "print(\"结果图像大小：\",image_filtered.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.4 多输入通道和多输出通道\n",
    "\n",
    "3D卷积操作的代码实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convolve3d(X, K,P=(0,0),S=(1,1)): \n",
    "    C,H,W = X.shape\n",
    "    C,F_h,F_w = K.shape\n",
    "    P_h,P_w = P[0],P[1]\n",
    "    S_h,S_w = S[0],S[1]   \n",
    "  \n",
    "    h = (H+2*P_h-F_h)//S_h+1\n",
    "    w = (W+2*P_w-F_w)//S_w+1\n",
    "    Y = np.zeros((h,w))            # convolution output\n",
    "   \n",
    "    if P_h!=0 or  P_w != 0:  \n",
    "        #X_padded = np.zeros((C,H + 2*P_h, W +2*P_w)) \n",
    "        #X_padded[:,P_h:-P_h, P_w:-P_w] = X\n",
    "        X_padded = np.pad(X,[(0,0),(P_h,P_h),(P_w,P_w)], mode='constant')\n",
    "    else:\n",
    "        X_padded = X\n",
    "    \n",
    "    for i in range(h):     # Loop over every pixel of the image  \n",
    "        hs = i*S_h\n",
    "        for j in range(w):   \n",
    "            ws = j*S_w\n",
    "            # element-wise multiplication of the kernel and the image\n",
    "            Y[i,j]=(K*X_padded[:,hs:hs+F_h, ws:ws+F_w]).sum()        \n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 86., 100.],\n",
       "       [128., 142.]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n",
    "[[11, 12, 13], [14, 15, 16], [17, 18, 19]]])\n",
    "K = np.array([[[1, 3], [2, 4]], [[4, -3], [2, 1]]])\n",
    "convolve3d(X,K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取一幅彩色图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image matrix size:  (330, 330, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage import io, transform\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "lenna_img = io.imread('lenna.png', as_gray=False)  # load the image as grayscale\n",
    "plt.imshow(lenna_img) #, cmap='gray')\n",
    "print('image matrix size: ', lenna_img.shape)      # print the size of image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 330, 330)\n",
      "(330, 330)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x254325702e0>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.moveaxis(lenna_img, -1, 0)  #np.rollaxis(lenna_img, 2, 0)\n",
    "kernel = np.array([[[-1,-1,-1],[-1,8,-1],[-1,-1,-1]],\n",
    "                   [[-1,-1,-1],[-1,8,-1],[-1,-1,-1]],\n",
    "                   [[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]] )\n",
    "edges = convolve3d(X,kernel,(1,1))\n",
    "print(X.shape)\n",
    "print(edges.shape)\n",
    "plt.imshow(edges,cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对3通道的彩色图像执行3D卷积操作，产生一个单通道的黑白图像："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.5 池化\n",
    "同卷积⼀样，**池化**(**pooling**)也是用一个固定形状窗口（称**池化窗口**）对准数据并计算出数据窗口的输出值。不同于卷积的输⼊数据和核的加权和，池化直接计算数据的池化窗口内元素的最⼤值或者平均值。\n",
    "\n",
    "和卷积操作一样，池化操作不仅有跨度，也可以对原始图像先填充再进行池化。类似于卷积操作，可以写出下面的对单通道的输入数据X执行池化操作的代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pool2d(X, pool, stride=(1,1),padding=(0,0), mode='max'):\n",
    "    pool_h, pool_w = pool\n",
    "    S_h,S_w = stride\n",
    "    P_h,P_w = padding\n",
    "    \n",
    "    #填充\n",
    "    if P_h or P_w:\n",
    "        X_padded = np.pad(X,[(P_h,P_h),(P_w,P_w)], mode='constant')\n",
    "    else:\n",
    "        X_padded = X\n",
    "    \n",
    "    #执行池化操作\n",
    "    Y_h,Y_w =  (X.shape[0]-pool_h+2*P_h)//S_h+1,(X.shape[1]-pool_w+2*P_w)//S_w+1        \n",
    "    Y = np.zeros((Y_h,Y_w ),dtype = X.dtype)\n",
    "    for i in range(Y.shape[0]):\n",
    "        hs = i*S_h\n",
    "        for j in range(Y.shape[1]):\n",
    "            ws = j*S_h\n",
    "            if mode == 'max':  #最大池化\n",
    "                Y[i, j] = X[hs: hs + pool_h, ws: ws + pool_w].max()\n",
    "            elif mode == 'avg':\n",
    "                Y[i, j] = X[hs: hs + pool_h, ws: ws + pool_w].mean()\n",
    "    return Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对二维矩阵执行跨度为3，窗口为$3\\times 3$的最大值和平均化池化的结果如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 9],\n",
       "       [9, 8]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= np.array([[2,3,0,7,9,5], [6,0,4,7,2,3], [8,1,0,3,2,6],\n",
    "             [7,6,1,5,2,8], [9,5,1,8,3,7], [2,4,1,8,6,5]])\n",
    "pool2d(X,(3,3),(3,3),(0,0),mode ='max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "执行平均池化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pool2d(X,(3,3),(3,3),(0,0),mode ='avg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于多通道的输入，只要对每个通道执行上面的单通道池化操作就可以了，下面的对多通道的输入数据X执行池化操作在原来的池化操作循环的外层增加了多通道进行遍历的循环（`for c in range(Y.shape[0])`）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pool(X, pool, stride=(1,1),padding=(0,0), mode='max'):\n",
    "    pool_h, pool_w = pool\n",
    "    S_h,S_w = stride\n",
    "    P_h,P_w = padding\n",
    "    \n",
    "    if P_h or P_w:\n",
    "        X_padded = np.pad(X,[(0,0),(P_h,P_h),(P_w,P_w)], mode='constant')\n",
    "    else:\n",
    "        X_padded = X\n",
    "    \n",
    "    Y_h,Y_w =  (X.shape[1]-pool_h+2*P_h)//S_h+1,(X.shape[1]-pool_w+2*P_w)//S_w+1\n",
    "        \n",
    "    Y = np.zeros((X.shape[0],Y_h,Y_w ),dtype = X.dtype)\n",
    "    print(X.shape)\n",
    "    print(Y.shape)\n",
    "  \n",
    "    for c in range(Y.shape[0]):\n",
    "        for i in range(Y.shape[1]):\n",
    "            hs = i*S_h\n",
    "            for j in range(Y.shape[2]):\n",
    "                ws = j*S_w\n",
    "                if mode == 'max':\n",
    "                    Y[c,i, j] = X[c,hs: hs + pool_h, ws: ws + pool_w].max()\n",
    "                elif mode == 'avg':\n",
    "                    Y[c,i, j] = X[c,hs: hs + pool_h, ws: ws + pool_w].mean()\n",
    "    return Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试上述的多通道输入的池化操作函数pool()："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2]\n",
      "  [ 3  4  5]\n",
      "  [ 6  7  8]]\n",
      "\n",
      " [[11  2  3]\n",
      "  [ 4  1 16]\n",
      "  [71  8  9]]]\n",
      "(2, 3, 3)\n",
      "(2, 1, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 4]],\n",
       "\n",
       "       [[11]]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X3= np.array([[[0, 1, 2], [3, 4, 5], [6, 7, 8]],\n",
    "[[11, 2, 3], [4, 1, 16], [71, 8, 9]]])\n",
    "print(X3)\n",
    "pool(X3,(2,2),(2,2),(0,0),mode ='max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用pool()函数和$5\\times5$窗口以跨度`(2,2)`对图像池化，产生的结果图像只有原图像一半大小。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 330, 330)\n",
      "(3, 163, 163)\n",
      "原图像大小： (3, 330, 330)\n",
      "结果图像大小： (163, 163, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.moveaxis(lenna_img, -1, 0)  #np.rollaxis(lenna_img, 2, 0)\n",
    "pooled_img = pool(img,[5,5],(2,2))\n",
    "pooled_img = np.moveaxis(pooled_img, 0, -1) # 将通道axis = 0移到最后的axis=-1的位置\n",
    "plt.imshow(pooled_img, cmap=plt.cm.gray)\n",
    "print(\"原图像大小：\",img.shape)\n",
    "print(\"结果图像大小：\",pooled_img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 卷积神经网络\n",
    "\n",
    "### 6.2.3 卷积层和池化层的反向求导及代码实现\n",
    "\n",
    "对于一维数据，完整的反向求导代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_backward(dz,x,w,p=0,s=1):\n",
    "    n, K = len(x),len(w)\n",
    "    o_n = 1 + (n + 2 * p - K) // s\n",
    "    assert(o_n==len(dz))    \n",
    "    \n",
    "    dx = np.zeros_like(x)\n",
    "    dw = np.zeros_like(w)\n",
    "    db = dz[:].sum()\n",
    "    \n",
    "    x_pad = np.pad(x, [(pad,pad)], 'constant')\n",
    "    dx_pad = np.zeros_like(x_pad)\n",
    "        \n",
    "    for i in range(o_n):\n",
    "        start = i * s\n",
    "        dw += x_pad[start:start+K]*dz[i]\n",
    "        dx_pad[start:start+K] += w*dz[i]\n",
    "    dx = dx_pad[pad:-pad]    \n",
    "    return dx, dw, db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下列代码测试一下上面的函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.4255293  -0.3763567  -0.34227539  0.29490764 -0.83732373]\n",
      "[ 0.50522405 -2.33230266 -0.87796042 -0.03246064  0.67446745]\n",
      "[-0.56864738 -0.65679696 -1.09889311]\n",
      "-2.6865774833459617\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(5)\n",
    "w = np.random.randn(3)\n",
    "stride = 2\n",
    "pad = 1\n",
    "dz = np.random.randn(5)\n",
    "\n",
    "print(dz)\n",
    "\n",
    "dx, dw, db = conv_backward(dz,x,w,1)\n",
    "print(dx)\n",
    "print(dw)\n",
    "print(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "表示卷积层的类Conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from init_weights import *\n",
    "\n",
    "class Layer:\n",
    "    def __init__(self):\n",
    "        self.params = None\n",
    "        pass\n",
    "    def forward(self, x):       \n",
    "        raise NotImplementedError\n",
    "    def backward(self, x, grad):        \n",
    "        raise NotImplementedError\n",
    "    def reg_grad(self,reg):\n",
    "        pass\n",
    "    def reg_loss(self,reg):\n",
    "        return 0.  \n",
    "    def reg_loss_grad(self,reg):\n",
    "        return 0       \n",
    "        \n",
    "class Conv(Layer):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size, stride=1,padding=0):\n",
    "            super().__init__()\n",
    "            self.C = in_channels\n",
    "            self.F = out_channels\n",
    "            self.K = kernel_size\n",
    "            self.S = stride\n",
    "            self.P = padding  \n",
    "            # filters is a 3d array with dimensions (num_filters, self.K, self.K)\n",
    "            # you can also use Xavier Initialization.\n",
    "            self.W = np.random.randn(self.F, self.C, self.K, self.K) #/(self.K*self.K)\n",
    "            self.b = np.random.randn(self.F,)\n",
    "            self.params = [self.W,self.b]\n",
    "            self.grads = [np.zeros_like(self.W),np.zeros_like(self.b)]\n",
    "            self.X = None\n",
    "            self.reset_parameters()\n",
    "            \n",
    "    def reset_parameters(self):\n",
    "        kaiming_uniform(self.W, a=math.sqrt(5))\n",
    "        if self.b is not None:\n",
    "            #fan_in, _ = calculate_fan_in_and_fan_out(self.K)\n",
    "            fan_in = self.C\n",
    "            bound = 1 / math.sqrt(fan_in)\n",
    "            self.b[:] = np.random.uniform(-bound,bound,(self.b.shape))\n",
    "            \n",
    "    def forward(self, X): \n",
    "        self.X = X\n",
    "        N, C, X_h, X_w = self.X.shape\n",
    "        F, _, F_h, F_w = self.W.shape    \n",
    "   #     print(self.X.shape,self.W.shape )\n",
    "        \n",
    "       \n",
    "        X_pad = np.pad(self.X, ((0,0), (0, 0), (self.P, self.P),(self.P, self.P)), mode='constant', constant_values=0)\n",
    "        \n",
    "        O_h = 1 + int((X_h + 2 * self.P - F_h) / self.S)\n",
    "        O_w = 1 + int((X_w + 2 * self.P - F_w) / self.S)\n",
    "        O = np.zeros((N, F, O_h, O_w))\n",
    "\n",
    "        for n in range(N):\n",
    "            for f in range(F):\n",
    "                for i in range(O_h):\n",
    "                    hs = i * self.S\n",
    "                    for j in range(O_w):\n",
    "                        ws = j * self.S                                         \n",
    "                        O[n, f, i, j] = (X_pad[n, :, hs:hs+F_h, ws:ws+F_w]*self.W[f]).sum() + self.b[f]                   \n",
    " \n",
    "        return O  \n",
    "\n",
    "    def __call__(self,X):\n",
    "        return self.forward(X)\n",
    "    \n",
    "    def backward(self,dZ):        \n",
    "        \"\"\" A naive implementation of the backward pass for a convolutional layer. \n",
    "        Inputs: - dout: Upstream derivatives.\n",
    "        - cache: A tuple of (x, w, b, conv_param) as in conv_forward_naive Returns a tuple of: \n",
    "        - dx: Gradient with respect to x - dw: Gradient with respect to w - db: Gradient with respect to b \"\"\"\n",
    "        N, F, Z_h, Z_w = dZ.shape\n",
    "        N, C, X_h, X_w = self.X.shape\n",
    "        F, _, F_h, F_w = self.W.shape \n",
    "        \n",
    "        pad  = self.P \n",
    "        \n",
    "        H_ = 1 + (X_h + 2 * pad - F_h) // self.S\n",
    "        W_ = 1 + (X_w + 2 * pad - F_w) // self.S\n",
    "               \n",
    "        \n",
    "        dX = np.zeros_like(self.X)\n",
    "        dW = np.zeros_like(self.W)\n",
    "        db = np.zeros_like(self.b)\n",
    "    \n",
    "        X_pad = np.pad(self.X, [(0,0), (0,0), (pad,pad), (pad,pad)], 'constant')\n",
    "        dX_pad = np.pad(dX, [(0,0), (0,0), (pad,pad), (pad,pad)], 'constant')\n",
    "                        \n",
    "        for n in range(N):\n",
    "            for f in range(F):\n",
    "                db[f] += dZ[n, f].sum()\n",
    "                for i in range(H_):\n",
    "                    hs = i * self.S\n",
    "                    for j in range(W_):\n",
    "                        ws = j * self.S       \n",
    "                        # w [f,c,i,j]  X[n,c,i,j]\n",
    "                        dW[f] += X_pad[n, :, hs:hs+F_h, ws:ws+F_w]*dZ[n, f, i, j] \n",
    "                        dX_pad[n, :, hs:hs+F_h, ws:ws+F_w] += self.W[f] * dZ[n, f, i, j]  \n",
    "       \n",
    "        # \"Unpad\"\n",
    "        dX = dX_pad[:, :, pad:pad+X_h, pad:pad+X_h]\n",
    "        \n",
    "\n",
    "        self.grads[0] += dW\n",
    "        self.grads[1] += db\n",
    "        return dX\n",
    "       # return dX, dW, db\n",
    "    \n",
    "     #--------添加正则项的梯度-----\n",
    "    def reg_grad(self,reg):\n",
    "        self.grads[0]+= 2*reg * self.W\n",
    "        \n",
    "    def reg_loss(self,reg):\n",
    "        return  reg*np.sum(self.W**2)\n",
    "    \n",
    "    def reg_loss_grad(self,reg):\n",
    "        self.grads[0]+= 2*reg * self.W\n",
    "        return  reg*np.sum(self.W**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 2, 5, 5)\n",
      "[[ 0.46362714 -0.83578144  0.40298519 -0.32152652  0.56616046]\n",
      " [-0.47878018  1.02346756  0.20004975  0.59663092  0.25253169]\n",
      " [-0.39733747 -0.08368194  0.52454712  0.54133918 -0.32698456]\n",
      " [ 0.47703053 -0.01967369  1.13655418  0.22321357  0.77693417]\n",
      " [-0.23944267  0.62971182 -0.38411731  0.42818679 -0.07566246]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "\n",
    "conv = Conv(3,2,3,1,1)\n",
    "f = conv.forward(x)\n",
    "print(f.shape)\n",
    "print(f[0,0],\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "反向求导backward()需要来自损失函数关于输出f的梯度$\\frac{\\partial \\mathcal{L} }{\\partial f}$，才能计算损失函数关于卷积的参数（$\\pmb W,\\pmb b$）和（$\\pmb X$）的梯度。为测试该方法，下面代码给它输入一个模拟的梯度，记为$df = \\frac{\\partial \\mathcal{L} }{\\partial \\pmb f}$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.30653407  0.07638048  0.36723181  1.23289919 -0.42285696]\n",
      " [ 0.08646441 -2.14246673 -0.83016886  0.45161595  1.10417433]\n",
      " [-0.28173627  2.05635552  1.76024923 -0.06065249 -2.413503  ]\n",
      " [-1.77756638 -0.77785883  1.11584111  0.31027229 -2.09424782]\n",
      " [-0.22876583  1.61336137 -0.37480469 -0.74996962  2.0546241 ]] \n",
      "\n",
      "[[-1.28063939e-02 -3.66152720e-01  8.60100186e-02 -1.22187599e-01\n",
      "  -9.82733000e-02]\n",
      " [ 1.56875134e-01 -1.50855186e-01 -9.11041554e-04 -3.84484585e-01\n",
      "   7.94984888e-02]\n",
      " [-5.68530426e-01  4.20951048e-01  5.41634150e-01  7.61553975e-01\n",
      "  -5.97223756e-01]\n",
      " [ 1.85998058e-01 -3.13055184e-01 -1.49268149e-01 -7.67989087e-01\n",
      "   3.10833619e-01]\n",
      " [ 3.84377541e-02  6.33352468e-01 -3.20074728e-01 -9.61297590e-01\n",
      "   9.84565706e-01]] \n",
      "\n",
      "[[-12.64870544   7.33773197  -3.47470049]\n",
      " [  4.76851832 -18.31687439   3.59104687]\n",
      " [ -3.28925017   0.94823861  -5.66853535]] \n",
      "\n",
      "[11.528173    7.46555585] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = np.random.randn(4, 2, 5, 5)\n",
    "dx= conv.backward(df)\n",
    "print(df[0,0],\"\\n\")\n",
    "print(dx[0,0],\"\\n\")\n",
    "print(conv.grads[0][0,0],\"\\n\")\n",
    "print(conv.grads[1],\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "卷积层的反向求导比较复杂，可用之前的util.py里的数值梯度函数numerical_gradient_from_df()函数计算数值梯度和反向求导的分析梯度进行比较，来检查反向求导是否正确。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.533440455314121e-11\n",
      "3.7474023883987684e-11\n",
      "3.998808228988793e-11\n"
     ]
    }
   ],
   "source": [
    "import util \n",
    "\n",
    "def f():\n",
    "   return conv.forward(x)\n",
    "\n",
    "dw_num = util.numerical_gradient_from_df(f,conv.W,df)\n",
    "diff_error = lambda x, y: np.max(np.abs(x - y)) \n",
    "print(diff_error(conv.grads[0],dw_num))\n",
    "\n",
    "db_num = util.numerical_gradient_from_df(lambda :conv.forward(x),conv.b,df)\n",
    "print(diff_error(conv.grads[1],db_num))\n",
    "\n",
    "dx_num = util.numerical_gradient_from_df(lambda :conv.forward(x),x,df)\n",
    "print(diff_error(dx,dx_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Pool(Layer):\n",
    "    def __init__(self, pool_param = (2,2,2)):\n",
    "        super().__init__()\n",
    "        self.pool_h,self.pool_w,self.stride = pool_param\n",
    "    def forward(self, x): \n",
    "        self.x = x    \n",
    "        N, C, H, W = x.shape\n",
    "        \n",
    "        pool_h,pool_w,stride= self.pool_h,self.pool_w,self.stride\n",
    "        \n",
    "        h_out = 1 + (H - pool_h) // stride\n",
    "        w_out = 1 + (W - pool_w) // stride         \n",
    "        out = np.zeros((N, C, h_out, w_out))\n",
    "        \n",
    "        for n in range(N):\n",
    "            for c in range(C):\n",
    "                for i in range(h_out):\n",
    "                    si = stride*i  \n",
    "                    for j in range(w_out):\n",
    "                        sj = stride*j \n",
    "                        x_win = x[n, c, si:si+pool_h, sj:sj+pool_w]  \n",
    "                        out[n,c,i,j] = np.max(x_win)        \n",
    "     \n",
    "        return out\n",
    "    \n",
    "    def backward(self,dout):\n",
    "        out = None\n",
    "        x = self.x\n",
    "        N, C, H, W = x.shape\n",
    "        kH,kW,stride = self.pool_h,self.pool_w,self.stride      \n",
    "        oH = 1 + (H - kH) // stride\n",
    "        oW = 1 + (W - kW) // stride\n",
    "       \n",
    "        dx = np.zeros_like(x)    \n",
    "  \n",
    "        for k in range(N):\n",
    "            for l in range(C):\n",
    "                for i in range(oH):\n",
    "                    si = stride * i\n",
    "                    for j in range(oW):\n",
    "                        sj = stride * j\n",
    "                        slice = x[k,l,si:si+kH,sj:sj+kW]\n",
    "                        slice_max = np.max(slice)\n",
    "                        dx[k,l,si:si+kH,sj:sj+kW] += (slice_max==slice)*dout[k,l,i,j] \n",
    "                    \n",
    "        return dx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同样，可以用数值梯度来验证pool类的分析梯度的正确性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2756319156460277e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(3, 2, 8, 8)\n",
    "df = np.random.randn(3, 2, 4, 4)\n",
    "\n",
    "pool = Pool((2,2,2))\n",
    "f = pool.forward(x)\n",
    "dx = pool.backward(df)\n",
    "\n",
    "dx_num = util.numerical_gradient_from_df(lambda :pool.forward(x),x,df)\n",
    "print(diff_error(dx,dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.4 卷积神经网络的实现\n",
    "\n",
    "在已经实现的卷积层、池化层和全连接层基础上，可以实现一个表示卷积神经网络的类的ConvNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetwork:   \n",
    "    def __init__(self):\n",
    "        self._layers = []\n",
    "        self._params = []\n",
    " \n",
    "    def add_layer(self, layer):      \n",
    "        self._layers.append(layer)\n",
    "        if layer.params: \n",
    "            for  i, _ in enumerate(layer.params):                         \n",
    "                self._params.append([layer.params[i],layer.grads[i]])  \n",
    "                \n",
    "    def forward(self, X): \n",
    "        for layer in self._layers:            \n",
    "            X = layer.forward(X) \n",
    "        return X   \n",
    "    \n",
    "    def __call__(self, X):\n",
    "        return self.forward(X)\n",
    "    \n",
    "    def predict(self, X):\n",
    "        p = self.forward(X)       \n",
    "        if p.ndim == 1:     #单样本\n",
    "            return np.argmax(ff)   \n",
    "        return np.argmax(p, axis=1)\n",
    "    \n",
    "    def backward(self,loss_grad,reg = 0.):\n",
    "        for i in reversed(range(len(self._layers))):\n",
    "            layer = self._layers[i] \n",
    "            loss_grad = layer.backward(loss_grad)\n",
    "            layer.reg_grad(reg) \n",
    "        return loss_grad\n",
    "    \n",
    "    def reg_loss(self,reg):\n",
    "        reg_loss = 0\n",
    "        for i in range(len(self._layers)):\n",
    "            reg_loss+=self._layers[i].reg_loss(reg)\n",
    "        return reg_loss\n",
    "    \n",
    "    def parameters(self): \n",
    "        return self._params\n",
    "    \n",
    "    def zero_grad(self):\n",
    "        for i,_ in enumerate(self._params):\n",
    "            self._params[i][1] *= 0. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的代码定义了一个简单的全连接神经网络对二维平面点集spiral数据集进行分类训练："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[    1, 1] loss: 1.123\n",
      "[  301, 301] loss: 0.292\n",
      "[  601, 601] loss: 0.259\n",
      "[  901, 901] loss: 0.249\n",
      "[ 1201, 1201] loss: 0.244\n",
      "[ 1501, 1501] loss: 0.242\n",
      "[ 1801, 1801] loss: 0.241\n",
      "[ 2101, 2101] loss: 0.241\n",
      "[ 2401, 2401] loss: 0.240\n",
      "[ 2701, 2701] loss: 0.240\n",
      "[ 3001, 3001] loss: 0.240\n",
      "[ 3301, 3301] loss: 0.240\n",
      "[ 3601, 3601] loss: 0.239\n",
      "[ 3901, 3901] loss: 0.239\n",
      "[ 4201, 4201] loss: 0.239\n",
      "[ 4501, 4501] loss: 0.239\n",
      "[ 4801, 4801] loss: 0.239\n",
      "0.99\n"
     ]
    }
   ],
   "source": [
    "import  util\n",
    "import train\n",
    "import data_set\n",
    "from Layers import *\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "nn = NeuralNetwork()\n",
    "nn.add_layer(Dense(2, 100))\n",
    "nn.add_layer(Relu())\n",
    "nn.add_layer(Dense(100, 3)) \n",
    "\n",
    "X,y = data_set.gen_spiral_dataset(N=100,D=2,K=3)\n",
    "\n",
    "learning_rate = 1e-1\n",
    "momentum = 0.9\n",
    "optimizer = train.SGD(nn.parameters(),learning_rate,momentum)\n",
    "\n",
    "epochs=5000\n",
    "batch_size = len(X)\n",
    "reg = 0.5e-3\n",
    "print_n=300\n",
    "\n",
    "losses = train.train_nn(nn,X,y,optimizer,util.cross_entropy_grad_loss,epochs,batch_size,reg,print_n)\n",
    "\n",
    "print(np.mean(nn.predict(X)==y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了测试卷积层，首先读取对Mnist手写数字训练集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 784)\n",
      "(50000, 1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "import pickle, gzip, urllib.request, json\n",
    "import numpy as np\n",
    "import os.path\n",
    "\n",
    "if not os.path.isfile(\"mnist.pkl.gz\"):\n",
    "    # Load the dataset\n",
    "    urllib.request.urlretrieve(\"http://deeplearning.net/data/mnist/mnist.pkl.gz\", \"mnist.pkl.gz\")\n",
    "    \n",
    "with gzip.open('mnist.pkl.gz', 'rb') as f:\n",
    "    train_set, valid_set, test_set = pickle.load(f, encoding='latin1')\n",
    "\n",
    "train_X, train_y = train_set\n",
    "print(train_X.shape)\n",
    "train_X = train_X.reshape((train_X.shape[0],1,28,28))\n",
    "print(train_X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义如下的卷积神经网络对Mnist手写数字识别进行分类训练，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[    1, 1] loss: 2.340\n",
      "[  101, 1] loss: 2.329\n",
      "[  201, 1] loss: 2.338\n",
      "[  301, 1] loss: 2.288\n",
      "[  401, 1] loss: 2.189\n",
      "[  501, 1] loss: 1.747\n",
      "[  601, 1] loss: 0.804\n",
      "[  701, 1] loss: 0.755\n",
      "1910.153290271759\n",
      "0.84064\n"
     ]
    }
   ],
   "source": [
    "import train\n",
    "#from NeuralNetwork import *\n",
    "import time\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "#nn = ConvNetwork()\n",
    "nn = NeuralNetwork()                    \n",
    "nn.add_layer(Conv(1,2,5,1,0))    # 1*28*28-> 2*24*24       # 1*2828-> 8*24*24\n",
    "nn.add_layer(Pool((2,2,2)))           #        ->2*12*12    #       ->8*12*12\n",
    "nn.add_layer(Conv(2,4,5,1,0))    #        ->4*8*8    ->16*8*8\n",
    "nn.add_layer(Pool((2,2,2)))           #        ->4*4*4      # ->16*4*4\n",
    "nn.add_layer(Dense(64, 100))\n",
    "nn.add_layer(Relu())\n",
    "nn.add_layer(Dense(100, 10)) \n",
    "\n",
    "learning_rate = 1e-3 #1e-1\n",
    "momentum = 0.9\n",
    "optimizer = train.SGD(nn.parameters(),learning_rate,momentum)\n",
    "\n",
    "epochs=1\n",
    "batch_size = 64\n",
    "reg = 1e-3\n",
    "print_n=100\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "X,y  =train_X,train_y\n",
    "\n",
    "losses = train.train_nn(nn,X,y,optimizer,util.cross_entropy_grad_loss,epochs,batch_size,reg,print_n)\n",
    "\n",
    "done = time.time()\n",
    "elapsed = done - start\n",
    "print(elapsed)\n",
    "\n",
    "print(np.mean(nn.predict(X)==y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10fe7847a30>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(losses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 快速卷积\n",
    "### 6.3.1  1D样本卷积的矩阵乘法\n",
    "### 6.3.2  2D样本卷积的矩阵乘法\n",
    "\n",
    "下面是完整的将一个已经填充后的四维张量转化为二维矩阵的代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def im2row(x, kH,kW, S=1):\n",
    "    N, C,H, W = x.shape\n",
    "    oH = (H - kH) // S + 1\n",
    "    oW = (W - kW) // S + 1\n",
    "    row = np.empty((N * oH * oW, kH * kW * C))\n",
    "    oSize =   oH*oW\n",
    "    \n",
    "    for h in range(oH):\n",
    "        hS = h * S\n",
    "        hS_kH = hS + kH\n",
    "        h_start = h*oW\n",
    "        for w in range(oW):\n",
    "            wS = w*S\n",
    "            patch = x[:,:,hS:hS_kH,wS:wS+kW]\n",
    "            row[h_start+w::oSize,:] = np.reshape(patch,(N,-1))\n",
    "    return row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用一个单样本多通道的四维张量测试一下这个函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(18).reshape(1,2,3,3)\n",
    "print(x)\n",
    "x_row = im2row(x,2,2)\n",
    "print(x_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再测试一下多样本多通道的4维张量："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(36).reshape(2,2,3,3)\n",
    "print(x)\n",
    "x_row = im2row(x,2,2)\n",
    "print(x_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以用矩阵乘法实现卷积层的卷积运算，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_forward(X, K, S=1, P=0):\n",
    "    N,C, H, W  = X.shape\n",
    "    F,C, kH,kW = K.shape\n",
    "    if P==0:\n",
    "        X_pad = X\n",
    "    else:\n",
    "        X_pad = np.pad(X, ((0, 0), (0, 0),(P, P), (P, P)), 'constant')\n",
    "   \n",
    "    X_row = im2row(X_pad, kH,kW, S)\n",
    "    \n",
    "    K_col = K.reshape(K.shape[0],-1).transpose()    \n",
    "    Z_row = np.dot(X_row, K_col)\n",
    "    \n",
    "    oH = (X_pad.shape[2] - kH) // S + 1\n",
    "    oW = (X_pad.shape[3] - kW) // S + 1\n",
    "    \n",
    "    Z = Z_row.reshape(N,oH,oW,-1)\n",
    "    Z = Z.transpose(0,3,1,2)\n",
    "    return Z    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试一下这个函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(9).reshape(1,1,3,3)+1\n",
    "k = np.arange(4).reshape(1,1,2,2)+1\n",
    "print(x)\n",
    "print(k)\n",
    "z = conv_forward(x,k)\n",
    "print(z.shape)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再测试一下多样本多通道的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(36).reshape(2,2,3,3)\n",
    "k = np.arange(16).reshape(2,2,2,2)\n",
    "z = conv_forward(x,k)\n",
    "print(z.shape)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3.4  2D卷积反向求导的矩阵乘法\n",
    "\n",
    "从$d\\pmb X_{row}$按照摊平的逆过程得到$d\\pmb X$可以实现为下面函数row2im()。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def row2im(dx_row,oH,oW,kH,kW,S):\n",
    "    nRow,K2C = dx_row.shape[0],dx_row.shape[1]\n",
    "    C = K2C//(kH*kW)\n",
    "    N = nRow//(oH*oW)     #样本个数    \n",
    "    oSize = oH*oW  \n",
    "    H = (oH - 1) * S + kH\n",
    "    W = (oW - 1) * S + kW\n",
    "    dx = np.zeros([N,C,H,W])\n",
    "    for i in range(oSize):\n",
    "        row = dx_row[i::oSize,:]  #N个行向量       \n",
    "        h_start = (i // oW) * S\n",
    "        w_start = (i % oW) * S     \n",
    "        dx[:,:,h_start:h_start+kH,w_start:w_start+kW] += row.reshape((N,C,kH,kW))  #np.reshape(row,(C,kH,kW))\n",
    "    return dx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "row2im()函数也可以写成下面的形式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def row2im(dx_row,oH,oW,kH,kW,S):\n",
    "    nRow,K2C = dx_row.shape[0],dx_row.shape[1]\n",
    "    C = K2C//(kH*kW)\n",
    "    N = nRow//(oH*oW)     #样本个数    \n",
    "    oSize = oH*oW  \n",
    "       \n",
    "    H = (oH - 1) * S + kH\n",
    "    W = (oW - 1) * S + kW\n",
    "    dx = np.zeros([N,C,H,W])\n",
    "    for h in range(oH):\n",
    "        hS = h * S\n",
    "        hS_kH = hS + kH\n",
    "        h_start = h*oW\n",
    "        for w in range(oW):\n",
    "            wS = w*S          \n",
    "            row =dx_row[h_start+w::oSize,:]                    \n",
    "            dx[:,:,hS:hS_kH,wS:wS+kW] += row.reshape(N,C,kH,kW)          \n",
    "    return dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def row2im(dx_row,oH,oW,kH,kW,S):\n",
    "    nRow,K2C = dx_row.shape[0],dx_row.shape[1]\n",
    "    C = K2C//(kH*kW)\n",
    "    N = nRow//(oH*oW)     #样本个数    \n",
    "    oSize = oH*oW  \n",
    "       \n",
    "    H = (oH - 1) * S + kH\n",
    "    W = (oW - 1) * S + kW\n",
    "    dx = np.zeros([N,C,H,W])\n",
    "    for h in range(oH):\n",
    "        hS = h * S\n",
    "        hS_kH = hS + kH\n",
    "        h_start = h*oW\n",
    "        for w in range(oW):\n",
    "            wS = w*S          \n",
    "            row =dx_row[h_start+w::oSize,:]                    \n",
    "            dx[:,:,hS:hS_kH,wS:wS+kW] += row.reshape(N,C,kH,kW)          \n",
    "    return dx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以用下面的代码测试一下上面的函数row2im()："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx_row [[ 0  1  2  3  4  5  6  7]\n",
      " [ 8  9 10 11 12 13 14 15]\n",
      " [16 17 18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29 30 31]\n",
      " [32 33 34 35 36 37 38 39]\n",
      " [40 41 42 43 44 45 46 47]\n",
      " [48 49 50 51 52 53 54 55]\n",
      " [56 57 58 59 60 61 62 63]\n",
      " [64 65 66 67 68 69 70 71]\n",
      " [ 0  1  2  3  4  5  6  7]\n",
      " [ 8  9 10 11 12 13 14 15]\n",
      " [16 17 18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29 30 31]\n",
      " [32 33 34 35 36 37 38 39]\n",
      " [40 41 42 43 44 45 46 47]\n",
      " [48 49 50 51 52 53 54 55]\n",
      " [56 57 58 59 60 61 62 63]\n",
      " [64 65 66 67 68 69 70 71]]\n",
      "(18, 8)\n",
      "(2, 2, 4, 4)\n",
      "dx[0,0,:,:]: [[  0.   9.  25.  17.]\n",
      " [ 26.  70. 102.  60.]\n",
      " [ 74. 166. 198. 108.]\n",
      " [ 50. 109. 125.  67.]]\n"
     ]
    }
   ],
   "source": [
    "kH,kW = 2,2\n",
    "oH,oW = 3,3\n",
    "N  = 1\n",
    "C = 2\n",
    "S  =1\n",
    "P = 0\n",
    "nRow = oH*oW*N\n",
    "K2C = C*kH*kW\n",
    "\n",
    "a = np.arange(nRow*K2C).reshape(nRow,K2C)\n",
    "#dx_row = np.arange(nRow*K2C).reshape(nRow,K2C)\n",
    "dx_row = np.vstack((a,a))\n",
    "print(\"dx_row\",dx_row)\n",
    "\n",
    "print(dx_row.shape)\n",
    "dx = row2im(dx_row,oH,oW,kH,kW,S)\n",
    "print(dx.shape)\n",
    "print(\"dx[0,0,:,:]:\",dx[0,0,:,:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于上面的讨论，卷积层的反向求导代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_backward(dZ,K,oH,oW,kH,kW,S=1,P=0):\n",
    "    #将dZ摊平为和Z_row形状一样的矩阵\n",
    "    F = dZ.shape[1]  # 将(N,F,oH,oW)转化为(N,oH,oW,F)\n",
    "    dZ_row = dZ.transpose(0,2,3,1).reshape(-1,F)\n",
    "    \n",
    "    #计算损失函数关于卷积核参数的梯度   \n",
    "    dK_col = np.dot(X_row.T,dZ_row) #X_row.T@dZ_row\n",
    "    dK_col = dK_col.transpose(1,0)\n",
    "    dK = dK_col.reshape(K.shape)\n",
    "    db = np.sum(dZ,axis=(0,2,3))\n",
    "    db = db.reshape(-1,F)\n",
    "    \n",
    "    K_col = K.reshape(K.shape[0],-1).transpose()  \n",
    "    dX_row = np.dot(dZ_row,K_col.T)\n",
    "    \n",
    "    dX_pad = row2im(dX_row,oH,oW,kH,kW,S)\n",
    "    if P == 0:\n",
    "        return dX_pad,dK,db\n",
    "    return dX_pad[:, :, P:-P, P:-P],dK,db    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的代码测试上述的卷积反向求导函数conv_backward()："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 4, 4)\n",
      "dX[0,0,:,:]: [[1512. 3150. 3298. 1718.]\n",
      " [3348. 6968. 7280. 3788.]\n",
      " [3804. 7904. 8216. 4268.]\n",
      " [2100. 4358. 4522. 2346.]]\n",
      "(4, 3, 2, 2)\n",
      "dW[0,0,:,:]: [[258. 294.]\n",
      " [402. 438.]]\n",
      "(1, 4)\n",
      "db: [[ 36 117 198 279]]\n"
     ]
    }
   ],
   "source": [
    "H,W  = 4,4\n",
    "kH,kW = 2,2\n",
    "oH,oW = 3,3\n",
    "N  = 1\n",
    "C = 3\n",
    "S  =1\n",
    "P = 0\n",
    "F = 4 \n",
    "\n",
    "dZ = np.arange(N*F*oH*oW).reshape(N,F,oH,oW)\n",
    "X =  np.arange(N*C*H*W).reshape(N,C,H,W)\n",
    "if P==0:\n",
    "    X_pad = X\n",
    "else:\n",
    "    X_pad = np.pad(X, ((0, 0), (0, 0),(P, P), (P, P)), 'constant')\n",
    "K = np.arange(F*C*kH*kW).reshape(F,C,kH,kW)\n",
    "\n",
    "X_row = im2row(X_pad, kH,kW, S)\n",
    "dX,dW,db = conv_backward(dZ,K,oH,oW,kH,kW,S,P)   \n",
    "print(dX.shape)\n",
    "print(\"dX[0,0,:,:]:\",dX[0,0,:,:])\n",
    "print(dW.shape)\n",
    "print(\"dW[0,0,:,:]:\",dW[0,0,:,:])\n",
    "print(db.shape)\n",
    "print(\"db:\",db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .3.5 基于坐标索引的快速卷积"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 1]\n",
      "[0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "kH,kW = 2,2\n",
    "i0 = np.repeat(np.arange(kH), kW) #行下标[0,1]沿着列方向重复:[0,0,1,1]\n",
    "print(i0)\n",
    "j0 = np.tile(np.arange(kW), kH)   #列下标[0,1]沿着行的方向拼接[0,1,0,1]\n",
    "print(j0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于下面的矩阵x，可以用这个i0、j0的组合索引得到左上角数据块的元素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['00' '01' '02' '03']\n",
      " ['10' '11' '12' '13']\n",
      " ['20' '21' '22' '23']\n",
      " ['30' '31' '32' '33']]\n",
      "['00' '00' '11' '11']\n"
     ]
    }
   ],
   "source": [
    "def idx_matrix(H,W):\n",
    "    a = np.empty((H,W), dtype='object')\n",
    "    for i in range(H):\n",
    "        for j in range(W):\n",
    "            a[i,j] = str(i)+str(j)\n",
    "    return a\n",
    "x = idx_matrix(4,4)\n",
    "print(x)\n",
    "print(x[i0,j0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般的，通道数为C的左上角数据块，其元素的行、列下标可以用下面代码生成："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "i0 = np.repeat(np.arange(kH), kW)\n",
    "i0 = np.tile(i0, C)\n",
    "j0 = np.tile(np.arange(kW), kH * C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于C=2，运行该代码:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 1 0 0 1 1]\n",
      "[0 1 0 1 0 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "C  = 2\n",
    "i0 = np.repeat(np.arange(kH), kW)\n",
    "i0 = np.tile(i0, C)\n",
    "j0 = np.tile(np.arange(kW), kH * C)\n",
    "print(i0)\n",
    "print(j0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同样，用生成一个数据块内元素行列坐标的代码可以生成这些跨度坐标："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 1 1 1 2 2 2]\n",
      "[0 1 2 0 1 2 0 1 2]\n"
     ]
    }
   ],
   "source": [
    "oH,oW=3,3\n",
    "i1 = S * np.repeat(np.arange(oH), oW)\n",
    "j1 = S * np.tile(np.arange(oW), oH)\n",
    "print(i1)\n",
    "print(j1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "左上角数据块的行列坐标，加上这些数据块的跨度坐标，就得到了所有数据块元素在原数据张量中的行列坐标，即："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i0: [0 0 1 1 0 0 1 1]\n",
      "i1: [0 0 0 1 1 1 2 2 2]\n",
      "[[0 0 0 1 1 1 2 2 2]\n",
      " [0 0 0 1 1 1 2 2 2]\n",
      " [1 1 1 2 2 2 3 3 3]\n",
      " [1 1 1 2 2 2 3 3 3]\n",
      " [0 0 0 1 1 1 2 2 2]\n",
      " [0 0 0 1 1 1 2 2 2]\n",
      " [1 1 1 2 2 2 3 3 3]\n",
      " [1 1 1 2 2 2 3 3 3]]\n"
     ]
    }
   ],
   "source": [
    "i = i0.reshape(-1, 1) + i1.reshape(1, -1)\n",
    "j = j0.reshape(-1, 1) + j1.reshape(1, -1)\n",
    "print(\"i0:\",i0)\n",
    "print(\"i1:\",i1)\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面是结合跨度坐标和数据块内元素下标得到所有数据块在原输入（单通道）张量中的行列下标的代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 1 1 1 2 2 2]\n",
      " [0 0 0 1 1 1 2 2 2]\n",
      " [1 1 1 2 2 2 3 3 3]\n",
      " [1 1 1 2 2 2 3 3 3]]\n",
      "[[0 1 2 0 1 2 0 1 2]\n",
      " [1 2 3 1 2 3 1 2 3]\n",
      " [0 1 2 0 1 2 0 1 2]\n",
      " [1 2 3 1 2 3 1 2 3]]\n"
     ]
    }
   ],
   "source": [
    "C=1\n",
    "S=1\n",
    "oH,oW=3,3\n",
    "kH,kW = 2,2\n",
    "\n",
    "i0 = np.repeat(np.arange(kH), kW)\n",
    "i0 = np.tile(i0, C)\n",
    "j0 = np.tile(np.arange(kW), kH * C)\n",
    "\n",
    "i1 = S * np.repeat(np.arange(oH), oW)\n",
    "j1 = S * np.tile(np.arange(oW), oH)\n",
    "\n",
    "i = i0.reshape(-1, 1) + i1.reshape(1, -1)\n",
    "j = j0.reshape(-1, 1) + j1.reshape(1, -1)\n",
    "print(i)\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果希望每个数据块索引下标成为矩阵的一行，则修改数据块下标和跨度下标是按行还是按列排列，即修改一下reshape代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 1]\n",
      " [0 0 1 1]\n",
      " [0 0 1 1]\n",
      " [1 1 2 2]\n",
      " [1 1 2 2]\n",
      " [1 1 2 2]\n",
      " [2 2 3 3]\n",
      " [2 2 3 3]\n",
      " [2 2 3 3]]\n",
      "[[0 1 0 1]\n",
      " [1 2 1 2]\n",
      " [2 3 2 3]\n",
      " [0 1 0 1]\n",
      " [1 2 1 2]\n",
      " [2 3 2 3]\n",
      " [0 1 0 1]\n",
      " [1 2 1 2]\n",
      " [2 3 2 3]]\n"
     ]
    }
   ],
   "source": [
    "C=1\n",
    "S=1\n",
    "oH,oW=3,3\n",
    "kH,kW = 2,2\n",
    "\n",
    "i0 = np.repeat(np.arange(kH), kW)\n",
    "i0 = np.tile(i0, C)\n",
    "j0 = np.tile(np.arange(kW), kH * C)\n",
    "\n",
    "i1 = S * np.repeat(np.arange(oH), oW)\n",
    "j1 = S * np.tile(np.arange(oW), oH)\n",
    "\n",
    "i = i0.reshape(1,-1) + i1.reshape(-1,1)\n",
    "j = j0.reshape(1,-1) + j1.reshape(-1,1)\n",
    "print(i)\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面代码计算一个数据块的通道坐标："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 1 1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "C=2\n",
    "k = np.repeat(np.arange(C), kH * kW).reshape(1,-1) #(-1, 1)\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "求参与卷积运算的数据块构成的扩展张量的所有元素在原数据张量中的通道坐标k、行下标i和列下标j。函数get_im2row_indices()可得到这些映射坐标(k,i,j)："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def get_im2row_indices(x_shape, kH, kW, S=1,P=0):  \n",
    "  N, C, H, W = x_shape\n",
    "  assert (H + 2 * P - kH) % S == 0\n",
    "  assert (W + 2 * P - kH) % S == 0\n",
    "  oH = (H + 2 * P - kH) // S + 1\n",
    "  oW = (W + 2 * P - kW) // S + 1\n",
    "\n",
    "  i0 = np.repeat(np.arange(kH), kW)\n",
    "  i0 = np.tile(i0, C)\n",
    "  i1 = S * np.repeat(np.arange(oH), oW)\n",
    "  j0 = np.tile(np.arange(kW), kH * C)\n",
    "  j1 = S * np.tile(np.arange(oW), oH)\n",
    "  #i = i0.reshape(-1, 1) + i1.reshape(1, -1)\n",
    "  #j = j0.reshape(-1, 1) + j1.reshape(1, -1)\n",
    "  i = i0.reshape(1,-1) + i1.reshape(-1,1)\n",
    "  j = j0.reshape(1,-1) + j1.reshape(-1,1)\n",
    "\n",
    "  k = np.repeat(np.arange(C), kH * kW).reshape(1,-1)\n",
    "  \n",
    "  return (k, i, j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的代码测试上述的函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 8)\n",
      "(9, 8)\n",
      "(9, 8)\n"
     ]
    }
   ],
   "source": [
    "H,W  = 4,4\n",
    "kH,kW = 2,2\n",
    "oH,oW = 3,3\n",
    "N  = 2\n",
    "C = 2\n",
    "S  =1\n",
    "P = 0\n",
    "F = 4 \n",
    "\n",
    "k, i, j = get_im2row_indices((N,C,H,W),kH,kW,S,P)  \n",
    "print(k.shape) \n",
    "print(i.shape)\n",
    "print(j.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了这个辅助函数，就可以轻松地从原数据张量生成数据块按行摊平的张量："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def im2row_indices(x, kH, kW, S=1,P=0):  \n",
    "  x_padded = np.pad(x, ((0, 0), (0, 0), (P, P), (P, P)), mode='constant')\n",
    "  k, i, j = get_im2row_indices(x.shape, kH, kW, S,P)    \n",
    "  rows = x_padded[:, k, i, j]           #每个样本的所有数据块\n",
    "  C = x.shape[1]\n",
    "  rows = rows.reshape(-1,kH * kW * C)   #第1个样本的所有数据块、第2个样本的所有数据块\n",
    "  return rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试一下这个函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[ 0  1  2  3]\n",
      "   [ 4  5  6  7]\n",
      "   [ 8  9 10 11]\n",
      "   [12 13 14 15]]\n",
      "\n",
      "  [[16 17 18 19]\n",
      "   [20 21 22 23]\n",
      "   [24 25 26 27]\n",
      "   [28 29 30 31]]]\n",
      "\n",
      "\n",
      " [[[32 33 34 35]\n",
      "   [36 37 38 39]\n",
      "   [40 41 42 43]\n",
      "   [44 45 46 47]]\n",
      "\n",
      "  [[48 49 50 51]\n",
      "   [52 53 54 55]\n",
      "   [56 57 58 59]\n",
      "   [60 61 62 63]]]]\n",
      "[[ 0  1  4  5 16 17 20 21]\n",
      " [ 1  2  5  6 17 18 21 22]\n",
      " [ 2  3  6  7 18 19 22 23]\n",
      " [ 4  5  8  9 20 21 24 25]\n",
      " [ 5  6  9 10 21 22 25 26]\n",
      " [ 6  7 10 11 22 23 26 27]\n",
      " [ 8  9 12 13 24 25 28 29]\n",
      " [ 9 10 13 14 25 26 29 30]\n",
      " [10 11 14 15 26 27 30 31]\n",
      " [32 33 36 37 48 49 52 53]\n",
      " [33 34 37 38 49 50 53 54]\n",
      " [34 35 38 39 50 51 54 55]\n",
      " [36 37 40 41 52 53 56 57]\n",
      " [37 38 41 42 53 54 57 58]\n",
      " [38 39 42 43 54 55 58 59]\n",
      " [40 41 44 45 56 57 60 61]\n",
      " [41 42 45 46 57 58 61 62]\n",
      " [42 43 46 47 58 59 62 63]]\n"
     ]
    }
   ],
   "source": [
    "X =  np.arange(N*C*H*W).reshape(N,C,H,W)\n",
    "X_row = im2row_indices(X,kH,kW,S,P)  \n",
    "print(X)\n",
    "print(X_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "或者反过来，将数据块按行摊平的张量转化为原数据张量的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def row2im_indices(rows, x_shape, kH, kW, S=1,P=0):\n",
    "  N, C, H, W = x_shape\n",
    "  H_pad, W_pad = H + 2 * P, W + 2 * P\n",
    "  x_pad = np.zeros((N, C,H_pad, W_pad), dtype=rows.dtype)\n",
    "  k, i, j = get_im2row_indices(x_shape, kH, kW, S,P)\n",
    "  \n",
    "  rows_reshaped = rows.reshape(N,-1,C * kH * kW)\n",
    " \n",
    "  np.add.at(x_pad, (slice(None), k, i, j), rows_reshaped)\n",
    "  if P == 0:\n",
    "    return x_pad\n",
    "  return x_pad[:, :, P:-P, P:-P]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx_row.shape (18, 8)\n",
      "dx.shape (2, 2, 4, 4)\n",
      "dx[0,0,:,:] [[ 0.  2.  4.  3.]\n",
      " [ 8. 20. 24. 14.]\n",
      " [16. 36. 40. 22.]\n",
      " [12. 26. 28. 15.]]\n",
      "dX.shape (2, 2, 4, 4)\n",
      "dX[0,0,:,:] [[ 0  2  4  3]\n",
      " [ 8 20 24 14]\n",
      " [16 36 40 22]\n",
      " [12 26 28 15]]\n",
      "[[[[  0   2   4   3]\n",
      "   [  8  20  24  14]\n",
      "   [ 16  36  40  22]\n",
      "   [ 12  26  28  15]]\n",
      "\n",
      "  [[ 16  34  36  19]\n",
      "   [ 40  84  88  46]\n",
      "   [ 48 100 104  54]\n",
      "   [ 28  58  60  31]]]\n",
      "\n",
      "\n",
      " [[[ 32  66  68  35]\n",
      "   [ 72 148 152  78]\n",
      "   [ 80 164 168  86]\n",
      "   [ 44  90  92  47]]\n",
      "\n",
      "  [[ 48  98 100  51]\n",
      "   [104 212 216 110]\n",
      "   [112 228 232 118]\n",
      "   [ 60 122 124  63]]]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "H,W  = 4,4\n",
    "kH,kW = 2,2\n",
    "oH,oW = 3,3\n",
    "N  = 2\n",
    "C = 2\n",
    "S  =1\n",
    "P = 0\n",
    "#F = 4 \n",
    "\n",
    "nRow = oH*oW*N\n",
    "K2C = C*kH*kW\n",
    "\n",
    "dx_row = X_row.copy()  #np.arange(nRow*K2C).reshape(nRow,K2C)\n",
    "print(\"dx_row.shape\",dx_row.shape)\n",
    "#print(\"dx_row\",dx_row)\n",
    "\n",
    "dx = row2im(dx_row,oH,oW,kH,kW,S)\n",
    "print(\"dx.shape\",dx.shape)\n",
    "print(\"dx[0,0,:,:]\",dx[0,0,:,:])\n",
    "\n",
    "#dx_row = dx_row.transpose()\n",
    "dX = row2im_indices(dx_row,(N,C,H,W),kH,kW,S,P)\n",
    "print(\"dX.shape\",dX.shape)\n",
    "print(\"dX[0,0,:,:]\",dX[0,0,:,:])\n",
    "print(dX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了上面的直接将多维张量摊平为矩阵的辅助函数（文件im2row.py中），可以编写一个基于快读卷积操作的卷积层："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Layers import *\n",
    "from im2row import *\n",
    "\n",
    "class Conv_fast():       \n",
    "    def __init__(self, in_channels, out_channels, kernel_size, stride=1,padding=0):\n",
    "            super().__init__()\n",
    "            self.C = in_channels\n",
    "            self.F = out_channels\n",
    "            self.kH = kernel_size\n",
    "            self.kW = kernel_size\n",
    "            self.S = stride\n",
    "            self.P = padding  \n",
    "            # filters is a 3d array with dimensions (num_filters, self.K, self.K)\n",
    "            # you can also use Xavier Initialization.\n",
    "            #self.K = np.random.randn(self.F, self.C, self.kH, self.kW) #/(self.K*self.K)\n",
    "            self.K = np.random.normal(0,1,(self.F, self.C, self.kH, self.kW))\n",
    "            self.b = np.zeros((1,self.F)) #,1))\n",
    "            self.params = [self.K,self.b]\n",
    "            self.grads = [np.zeros_like(self.K),np.zeros_like(self.b)]\n",
    "            self.X = None\n",
    "            self.reset_parameters()\n",
    "            \n",
    "    def reset_parameters(self):\n",
    "        kaiming_uniform(self.K, a=math.sqrt(5))\n",
    "        if self.b is not None:\n",
    "            #fan_in, _ = calculate_fan_in_and_fan_out(self.K)\n",
    "            fan_in = self.C\n",
    "            bound = 1 / math.sqrt(fan_in)\n",
    "            self.b[:] = np.random.uniform(-bound,bound,(self.b.shape))\n",
    "            \n",
    "            \n",
    "    def forward(self,X):      \n",
    "         #转化为多通道\n",
    "        self.X = X\n",
    "        if len(X.shape)==1:\n",
    "            X = X.reshape(X.shape[0],1,1,1)\n",
    "        elif len(X.shape)==2:\n",
    "            X = X.reshape(X.shape[0],X.shape[1],1,1)\n",
    "  \n",
    "        self.N,self.H,self.W = X.shape[0], X.shape[2], X.shape[3]\n",
    "        S,P,kH,kW = self.S, self.P,self.kH,self.kW\n",
    "        self.oH = (self.H - kH + 2*P)// S + 1\n",
    "        self.oW = (self.W - kW + 2*P)// S + 1   \n",
    "        \n",
    "        X_shape = (self.N,self.C,self.H,self.W)\n",
    "\n",
    "        if True:\n",
    "            self.X_row = im2row_indices(X,self.kH,self.kW,S=self.S,P=self.P)        \n",
    "        else:\n",
    "            if P==0:\n",
    "                X_pad = X\n",
    "            else:\n",
    "                X_pad = np.pad(X, ((0, 0), (0, 0),(P, P), (P, P)), 'constant')\n",
    "            self.X_row = im2row(X_pad, kH,kW, S)    \n",
    "       \n",
    "        K_col = self.K.reshape(self.F,-1).transpose()         \n",
    "        Z_row =  self.X_row @ K_col   + self.b #W_row @ self.X_row + self.b   \n",
    "       \n",
    "        Z = Z_row.reshape(self.N,self.oH,self.oW,-1)\n",
    "        Z = Z.transpose(0,3,1,2)           \n",
    "        return Z\n",
    "\n",
    "    def __call__(self,x):\n",
    "         return self.forward(x)\n",
    "\n",
    "    def backward(self,dZ): \n",
    "        \n",
    "        if len(dZ.shape)<=2:\n",
    "            dZ = dZ.reshape(dZ.shape[0],-1,self.oH,self.oW)\n",
    "        K = self.K\n",
    "        #将dZ摊平为和Z_row形状一样的矩阵\n",
    "        F = dZ.shape[1]  # 将(N,F,oH,oW)转化为(N,oH,oW,F)\n",
    "        assert(F==self.F)\n",
    "        dZ_row = dZ.transpose(0,2,3,1).reshape(-1,F)\n",
    "        \n",
    "        #计算损失函数关于卷积核参数的梯度   \n",
    "        dK_col = np.dot(self.X_row.T,dZ_row) #X_row.T@dZ_row\n",
    "        dK_col = dK_col.transpose(1,0)  #将F通道轴从axis=1变为axis=0\n",
    "        dK = dK_col.reshape(self.K.shape)\n",
    "        db = np.sum(dZ,axis=(0,2,3))\n",
    "        db = db.reshape(-1,F)\n",
    "\n",
    "        #计算损失函数关于卷积层输入的梯度 \n",
    "        \n",
    "        K_col = K.reshape(K.shape[0],-1).transpose()  #摊平\n",
    "        dX_row = np.dot(dZ_row,K_col.T)\n",
    "\n",
    "        if True:\n",
    "            X_shape = (self.N,self.C,self.H,self.W)\n",
    "            dX = row2im_indices(dX_row,X_shape,self.kH,self.kW,S =self.S,P = self.P)     \n",
    "        else:\n",
    "            dX_pad = row2im(dX_row,oH,oW,kH,kW,S)\n",
    "            if P == 0:\n",
    "                dX =  dX_pad\n",
    "            dX = dX_pad[:, :, P:-P, P:-P]    \n",
    "        \n",
    "        dX = dX.reshape(self.X.shape)\n",
    "        self.grads[0] += dK\n",
    "        self.grads[1] += db\n",
    "                 \n",
    "        return dX\n",
    "    \n",
    "     #--------添加正则项的梯度-----\n",
    "    def reg_grad(self,reg):\n",
    "        self.grads[0]+= 2*reg * self.K\n",
    "        \n",
    "    def reg_loss(self,reg):\n",
    "        return  reg*np.sum(self.K**2)\n",
    "    \n",
    "    def reg_loss_grad(self,reg):\n",
    "        self.grads[0]+= 2*reg * self.K\n",
    "        return  reg*np.sum(self.K**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 梯度检验\n",
    "\n",
    "同样，对这个卷积层可以用梯度检验检验代码是否正确："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.198542114313848e-07\n",
      "dW [[[[ 1.90074619e+00  8.89343674e-03 -1.41133050e+00]\n",
      "   [ 9.52031925e+00  4.30414869e+00  3.47828606e-01]\n",
      "   [ 8.00613543e-01  2.61092491e+00  5.47100877e+00]]\n",
      "\n",
      "  [[ 9.93385161e-01 -9.75200567e-01  8.38974052e-01]\n",
      "   [-1.54004886e+00 -2.61334505e+00 -3.25602882e+00]\n",
      "   [-4.56745810e+00 -5.54062088e+00 -8.84416935e+00]]\n",
      "\n",
      "  [[ 1.13369140e-01  3.49324890e+00 -4.08963800e+00]\n",
      "   [ 3.03261514e+00  2.70653447e+00 -3.96369196e-01]\n",
      "   [ 7.68198498e-01 -6.88892583e+00  1.80311820e+00]]]\n",
      "\n",
      "\n",
      " [[[ 2.70714697e+00  7.79058813e+00  3.44251333e+00]\n",
      "   [-4.95036615e+00 -3.93298244e+00 -2.54547136e+00]\n",
      "   [ 6.04601218e-01 -1.59437990e+00 -8.97042348e+00]]\n",
      "\n",
      "  [[ 6.76768113e+00 -2.18631886e+00 -4.03541514e-02]\n",
      "   [ 1.83888738e+00  4.33482794e-01 -2.24258818e+00]\n",
      "   [ 6.27679348e+00 -4.88206234e+00 -3.91765947e+00]]\n",
      "\n",
      "  [[ 4.27699392e-01 -1.72104452e+00  2.71063625e+00]\n",
      "   [ 2.87661696e+00 -7.94690148e+00  1.76789529e+00]\n",
      "   [-2.59863725e+00 -1.40208579e+00 -1.58401559e+00]]]]\n",
      "dW_num [array([[[ 1.90074618e+00,  8.89344420e-03, -1.41133050e+00],\n",
      "        [ 9.52031925e+00,  4.30414867e+00,  3.47828603e-01],\n",
      "        [ 8.00613542e-01,  2.61092492e+00,  5.47100876e+00]],\n",
      "\n",
      "       [[ 9.93385164e-01, -9.75200571e-01,  8.38974053e-01],\n",
      "        [-1.54004886e+00, -2.61334505e+00, -3.25602883e+00],\n",
      "        [-4.56745811e+00, -5.54062089e+00, -8.84416935e+00]],\n",
      "\n",
      "       [[ 1.13369140e-01,  3.49324890e+00, -4.08963800e+00],\n",
      "        [ 3.03261514e+00,  2.70653447e+00, -3.96369195e-01],\n",
      "        [ 7.68198497e-01, -6.88892583e+00,  1.80311820e+00]]]), array([[[ 2.70714696,  7.79058813,  3.44251333],\n",
      "        [-4.95036615, -3.93298244, -2.54547137],\n",
      "        [ 0.60460121, -1.59437991, -8.97042349]],\n",
      "\n",
      "       [[ 6.76768112, -2.18631886, -0.04035415],\n",
      "        [ 1.83888738,  0.4334828 , -2.24258818],\n",
      "        [ 6.27679348, -4.88206233, -3.91765946]],\n",
      "\n",
      "       [[ 0.42769939, -1.72104452,  2.71063624],\n",
      "        [ 2.87661696, -7.94690149,  1.76789529],\n",
      "        [-2.59863725, -1.40208579, -1.5840156 ]]])]\n"
     ]
    }
   ],
   "source": [
    "import util\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "N,C,H,W = 4,3,5,5\n",
    "F,kH,kW = 6,3,3\n",
    "oH,oW = 3,3\n",
    "x = np.random.randn(N,C,H,W)\n",
    "y = np.random.randn(N,F,oH,oW) \n",
    "\n",
    "conv = Conv_fast(C,F,kH,1,0)\n",
    "f = conv.forward(x)\n",
    "\n",
    "loss,do = util.mse_loss_grad(f,y)\n",
    "dx = conv.backward(do)\n",
    "\n",
    "def loss_f():\n",
    "    f = conv.forward(x)\n",
    "    loss,do = util.mse_loss_grad(f,y)\n",
    "    return loss\n",
    "\n",
    "dW_num = util.numerical_gradient(loss_f,conv.params[0],1e-6)\n",
    "\n",
    "diff_error = lambda x, y: np.max(np.abs(x - y)/(np.maximum(1e-8, np.abs(x) + np.abs(y) )) )\n",
    "print(diff_error(conv.grads[0],dW_num))\n",
    "print(\"dW\",conv.grads[0][:2])\n",
    "print(\"dW_num\",dW_num[:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 与非加速卷积的时间比较 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了比较快速卷积与之前的非加速卷积的时间效率，用之前的同样的卷积神经网络对MNist手写数字识别问题进行训练："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "419.67770290374756\n",
      "46.87388467788696\n"
     ]
    }
   ],
   "source": [
    "from Layers import *\n",
    "import time\n",
    "np.random.seed(0)\n",
    "\n",
    "#N,C,H,W = 64,256,64,64\n",
    "#F,kH= 128,5\n",
    "N,C,H,W = 128,16,64,64\n",
    "F,kH= 32,5\n",
    "x = np.random.randn(N,C,H,W)\n",
    "oH = H-kH+1\n",
    "do = np.random.randn(N,F,oH,oH)\n",
    "\n",
    "start = time.time()\n",
    "conv = Conv(C,F,kH)\n",
    "f = conv(x)\n",
    "conv.backward(do)\n",
    "done = time.time()\n",
    "elapsed = done - start\n",
    "print(elapsed)\n",
    "\n",
    "start = time.time()\n",
    "conv = Conv_fast(C,F,kH)\n",
    "f = conv(x)\n",
    "conv.backward(do)\n",
    "done = time.time()\n",
    "elapsed = done - start\n",
    "print(elapsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将前面的对MNist手写数字分类的卷积神经网络的卷积换成快速卷积，看一下时间效率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 784)\n",
      "(50000, 1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "import pickle, gzip, urllib.request, json\n",
    "import numpy as np\n",
    "import os.path\n",
    "\n",
    "if not os.path.isfile(\"mnist.pkl.gz\"):\n",
    "    # Load the dataset\n",
    "    urllib.request.urlretrieve(\"http://deeplearning.net/data/mnist/mnist.pkl.gz\", \"mnist.pkl.gz\")\n",
    "    \n",
    "with gzip.open('mnist.pkl.gz', 'rb') as f:\n",
    "    train_set, valid_set, test_set = pickle.load(f, encoding='latin1')\n",
    "\n",
    "train_X, train_y = train_set\n",
    "print(train_X.shape)\n",
    "train_X = train_X.reshape((train_X.shape[0],1,28,28))\n",
    "print(train_X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义一个卷积神经网络并训练："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import train\n",
    "from NeuralNetwork import *\n",
    "import time\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "nn = NeuralNetwork()                       \n",
    "nn.add_layer(Conv_fast(1,2,5,1,0))    # 1*2828-> 2*24*24       # 1*2828-> 8*24*24\n",
    "nn.add_layer(Pool((2,2,2)))           #        ->2*12*12    #       ->8*12*12\n",
    "nn.add_layer(Conv_fast(2,4,5,1,0))    #        ->4*8*8    ->16*8*8\n",
    "nn.add_layer(Pool((2,2,2)))           #        ->4*4*4      # ->16*4*4\n",
    "nn.add_layer(Dense(64, 100))\n",
    "nn.add_layer(Relu())\n",
    "nn.add_layer(Dense(100, 10)) \n",
    "\n",
    "learning_rate = 1e-3 #1e-1\n",
    "momentum = 0.9\n",
    "optimizer = train.SGD(nn.parameters(),learning_rate,momentum)\n",
    "\n",
    "epochs=1\n",
    "batch_size = 64\n",
    "reg = 1e-3\n",
    "print_n=100\n",
    "\n",
    "start = time.time()\n",
    "X,y  =train_X,train_y\n",
    "losses = train.train_nn(nn,X,y,optimizer,util.cross_entropy_grad_loss,epochs,batch_size,reg,print_n)\n",
    "done = time.time()\n",
    "elapsed = done - start\n",
    "print(elapsed)\n",
    "\n",
    "print(np.mean(nn.predict(X)==y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]\n",
      "  [12 13 14 15]]\n",
      "\n",
      " [[16 17 18 19]\n",
      "  [20 21 22 23]\n",
      "  [24 25 26 27]\n",
      "  [28 29 30 31]]]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(32).reshape(2,4,4)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  4  5  2  3  6  7]\n",
      " [16 17 20 21 18 19 22 23]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  4,  5],\n",
       "       [ 2,  3,  6,  7],\n",
       "       [16, 17, 20, 21],\n",
       "       [18, 19, 22, 23]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = [0,0,1,1,0,0,1,1]\n",
    "j = [0,1,0,1,2,3,2,3]\n",
    "y = x[:,k,j]\n",
    "print(y)\n",
    "y.reshape(-1,4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12443648"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "192*28*28*16+5*5*16*28*28*32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120422400"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5*5*192*28*28*32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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